Abstract:

Poyang Lake is the largest freshwater lake in China, and has an internationally recognized wetland system. It receives inflows predominantly from five major rivers (i.e. the Ganjiang, Fuhe, Xinjiang, Raohe and Xiushui Rivers) within its drainage catchment. The Ganjiang, Fuhe and Xinjiang Rivers contribute approximately 89% of the lakes inflow from its drainage catchment area, and the remaining 11% is mainly made up of inflows from the Raohe and Xiushui Rivers. Poyang Lake is connected to the Yangtze River through a narrow channel at Hukou at its northern end, and the Yangtze River plays a complementary role in controlling outflows.Because the Poyang Lake has a nonlinear response to the river discharges from its drainage basin and the Yangtze River, the highly complexity and nonlinear characteristics determine that the threelayer back propagation neural network (BPNN) has the ability to simulate the lake water level responses. In this study, the water level timeseries of the Hukou, Xingzi, Duchang, Tangyin and Kangshan were used as the target variables for the BPNN construction under different model scenarios. Results indicate that both discharges from the catchment rivers and the Yangtze River are considered, thedetermination coefficients R2 and NashSutcliffe efficiency Ens for all gauging stations can reach 090 and the Root Mean Square Error RMSE is in range of 050-10 m during the BPNN training and testing phases, while the simulated results reveal that the Ens and R2 are lower than 050, and RMSE is significantly increased in range of 124-288 m under the condition of neglected the Yangtze River discharges. The BPNN can be used to combine the catchment hydrological models, which can provide an alternative tool for predicting the lake water levels in response to catchment river discharges under climate and landuse changes. The BPNN also can be used as an effective modeling tool to solve some hot issues in the Poyang Lake, such as how to quantitatively distinguish the individual contributions of catchment rivers and the Yangtze River to the flood and low water levels. At the same time, the model users should select long timeseries dataset with best data consistency to train and test the BPNN model